Kernel Least Mean Square Algorithm
نویسنده
چکیده
A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily used in high dimensional spaces and particularly in reproducing kernel Hilbert spaces (RKHS) to derive nonlinear self-regularized, stable algorithms.
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